Related Work. Collaborative ML is a rich and novel field which uses solution concepts from cooperative game theory and mechanism design.
The Shapley value is a commonly adopted solution concept to formalize a notion of fairness in quantifying the contributions of self-interested parties (e.g., via their shared data) [1]}, [2]}.
This line of research inspires several data valuation methods using the Shapley value [3]}, [4]}, [5]}, [6]}, the core [7]}, influence functions [8]}, and volume [9]}.
Previous works have used concepts from mechanism design to elicit truthful reporting [10]}, [11]} and to incentivize sharing data and/or model parameters in federated learning [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}.
Other works have addressed data privacy [19]}, [20]}, adversarial robustness [21]}, [22]}, communication efficiency [19]}, and fairness in Bayesian optimization [24]}.
Compared to existing works which have mainly focused on supervised learning, our work investigates a novel task- and model-agnostic setting through the CGM framework that distributes synthetic data as rewards, which to the best of our knowledge has not been considered in the literature.
